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2024
Patent
Open Access
Caching historical embeddings in conversational search
Ophir Frieder, Ida Mele, Cristina Ioana Muntean, Franco Maria Nardini, Raffaele Perego, Nicola Tonellotto
A method and system are described for improving the speed and efficiency of obtaining conversational search results. A user may speak a phrase to perform a conversational search or a series of phrases to perform a series of searches. These spoken phrases may be enriched by context and then converted into a query embedding. A similarity between the query embedding and document embeddings is used to determine the search results including a query cutoff number of documents and a cache cutoff number of documents. A second search phrase may use the cache of documents along with comparisons of the returned documents and the first query embedding to determine the quality of the cache for responding to the second search query. If the results are high-quality then the search may proceed much more rapidly by applying the second query only to the cached documents rather than to the server.
Ophir Frieder, Ida Mele, Cristina Ioana Muntean, Franco Maria Nardini, Raffaele Perego, Nicola Tonellotto
A method and system are described for improving the speed and efficiency of obtaining conversational search results. A user may speak a phrase to perform a conversational search or a series of phrases to perform a series of searches. These spoken phrases may be enriched by context and then converted into a query embedding. A similarity between the query embedding and document embeddings is used to determine the search results including a query cutoff number of documents and a cache cutoff number of documents. A second search phrase may use the cache of documents along with comparisons of the returned documents and the first query embedding to determine the quality of the cache for responding to the second search query. If the results are high-quality then the search may proceed much more rapidly by applying the second query only to the cached documents rather than to the server.
2023
Conference article
Open Access
Commonsense injection in conversational systems: an adaptable framework for query expansion
Rocchietti G., Frieder O., Muntean Cristina, Nardini F. M., Perego R.
Recent advancements in conversational agents are leading a paradigm shift in how people search for their information needs, from text queries to entire spoken conversations. This paradigm shift poses a new challenge: a single question may lack the context driven by the entire conversation. We propose and evaluate a framework to deal with multi-turn conversations with the injection of commonsense knowledge. Specifically, we propose a novel approach for conversational search that uses pre-trained large language models and commonsense knowledge bases to enrich queries with relevant concepts. Our framework comprises a generator of candidate concepts related to the context of the conversation and a selector for deciding which candidate concept to add to the current utterance to improve retrieval effectiveness. We use the TREC CAsT datasets and ConceptNet to show that our framework improves retrieval performance by up to 82% in terms of Recall@200 and up to 154% in terms of NDCG@3 as compared to the performance achieved by the original utterances in the conversations.Project(s): EFRA
Rocchietti G., Frieder O., Muntean Cristina, Nardini F. M., Perego R.
Recent advancements in conversational agents are leading a paradigm shift in how people search for their information needs, from text queries to entire spoken conversations. This paradigm shift poses a new challenge: a single question may lack the context driven by the entire conversation. We propose and evaluate a framework to deal with multi-turn conversations with the injection of commonsense knowledge. Specifically, we propose a novel approach for conversational search that uses pre-trained large language models and commonsense knowledge bases to enrich queries with relevant concepts. Our framework comprises a generator of candidate concepts related to the context of the conversation and a selector for deciding which candidate concept to add to the current utterance to improve retrieval effectiveness. We use the TREC CAsT datasets and ConceptNet to show that our framework improves retrieval performance by up to 82% in terms of Recall@200 and up to 154% in terms of NDCG@3 as compared to the performance achieved by the original utterances in the conversations.Project(s): EFRA
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2023
Conference article
Open Access
A spatial approach to predict performance of conversational search systems
Faggioli G, Ferro N, Muntean C, Perego R, Tonellotto N
Recent advancements in Information Retrieval and Natural Language Processing have led to significant developments in the way users interact with search engines, with traditional one-shot textual queries being replaced by multi-turn conversations. As a highly interactive search scenario, Conversational Search (CS) can significantly benefit from Query Performance Prediction (QPP) techniques. However, the application of QPP in the CS domain is a relatively new field and requires proper framing. This study proposes a set of spatial-based QPP models, designed to work effectively in the conversational search domain, where dense neural retrieval models are the most common approach and query cutoffs are small. The proposed QPP approaches are shown to improve the predictive performance over the state-of-the-art in different scenarios and collections, highlighting the utility of QPP in the CS domain.Source: CEUR WORKSHOP PROCEEDINGS, pp. 41-46. Pisa, Italy, 8-9/06/2023
Faggioli G, Ferro N, Muntean C, Perego R, Tonellotto N
Recent advancements in Information Retrieval and Natural Language Processing have led to significant developments in the way users interact with search engines, with traditional one-shot textual queries being replaced by multi-turn conversations. As a highly interactive search scenario, Conversational Search (CS) can significantly benefit from Query Performance Prediction (QPP) techniques. However, the application of QPP in the CS domain is a relatively new field and requires proper framing. This study proposes a set of spatial-based QPP models, designed to work effectively in the conversational search domain, where dense neural retrieval models are the most common approach and query cutoffs are small. The proposed QPP approaches are shown to improve the predictive performance over the state-of-the-art in different scenarios and collections, highlighting the utility of QPP in the CS domain.Source: CEUR WORKSHOP PROCEEDINGS, pp. 41-46. Pisa, Italy, 8-9/06/2023
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ceur-ws.org | CNR IRIS | CNR IRIS | CNR IRIS
2022
Conference article
Open Access
The 2nd workshop on Mixed-Initiative ConveRsatiOnal Systems (MICROS)
Mele I., Muntean C. I., Aliannejadi M., Voskarides N.
The Mixed-Initiative ConveRsatiOnal Systems workshop (MICROS) aims at bringing novel ideas and investigating new solutions on conversational assistant systems. The increasing popularity of personal assistant systems, as well as smartphones, has changed the way users access online information, posing new challenges for information seeking and filtering. MICROS has a particular focus on mixed-initiative conversational systems, namely, systems that can provide answers in a proactive way (e.g., asking for clarification or proposing possible interpretations for ambiguous and vague requests). We invite people working on conversational systems or interested in the workshop topics to send us their position and research manuscripts.Source: CIKM '22 - 31st ACM International Conference on Information & Knowledge Management, pp. 5173–5174, Atlanta, USA, 17-21/10/2022
DOI: 10.1145/3511808.3557938
Metrics:
Mele I., Muntean C. I., Aliannejadi M., Voskarides N.
The Mixed-Initiative ConveRsatiOnal Systems workshop (MICROS) aims at bringing novel ideas and investigating new solutions on conversational assistant systems. The increasing popularity of personal assistant systems, as well as smartphones, has changed the way users access online information, posing new challenges for information seeking and filtering. MICROS has a particular focus on mixed-initiative conversational systems, namely, systems that can provide answers in a proactive way (e.g., asking for clarification or proposing possible interpretations for ambiguous and vague requests). We invite people working on conversational systems or interested in the workshop topics to send us their position and research manuscripts.Source: CIKM '22 - 31st ACM International Conference on Information & Knowledge Management, pp. 5173–5174, Atlanta, USA, 17-21/10/2022
DOI: 10.1145/3511808.3557938
Metrics:
See at:
ISTI Repository | dl.acm.org | doi.org | CNR ExploRA
2022
Journal article
Open Access
Caching historical embeddings in conversational search
Frieder O., Mele I., Muntean C., Nardini F. M., Perego R., Tonellotto N.
Rapid response, namely low latency, is fundamental in search applications; it is particularly so in interactive search sessions, such as those encountered in conversational settings. An observation with a potential to reduce latency asserts that conversational queries exhibit a temporal locality in the lists of documents retrieved. Motivated by this observation, we propose and evaluate a client-side document embedding cache, improving the responsiveness of conversational search systems. By leveraging state-of-the-art dense retrieval models to abstract document and query semantics, we cache the embeddings of documents retrieved for a topic introduced in the conversation, as they are likely relevant to successive queries. Our document embedding cache implements an efficient metric index, answering nearest-neighbor similarity queries by estimating the approximate result sets returned. We demonstrate the efficiency achieved using our cache via reproducible experiments based on TREC CAsT datasets, achieving a hit rate of up to 75% without degrading answer quality. Our achieved high cache hit rates significantly improve the responsiveness of conversational systems while likewise reducing the number of queries managed on the search back-end.Source: ACM TRANSACTIONS ON THE WEB, vol. 18 (issue 4)
Frieder O., Mele I., Muntean C., Nardini F. M., Perego R., Tonellotto N.
Rapid response, namely low latency, is fundamental in search applications; it is particularly so in interactive search sessions, such as those encountered in conversational settings. An observation with a potential to reduce latency asserts that conversational queries exhibit a temporal locality in the lists of documents retrieved. Motivated by this observation, we propose and evaluate a client-side document embedding cache, improving the responsiveness of conversational search systems. By leveraging state-of-the-art dense retrieval models to abstract document and query semantics, we cache the embeddings of documents retrieved for a topic introduced in the conversation, as they are likely relevant to successive queries. Our document embedding cache implements an efficient metric index, answering nearest-neighbor similarity queries by estimating the approximate result sets returned. We demonstrate the efficiency achieved using our cache via reproducible experiments based on TREC CAsT datasets, achieving a hit rate of up to 75% without degrading answer quality. Our achieved high cache hit rates significantly improve the responsiveness of conversational systems while likewise reducing the number of queries managed on the search back-end.Source: ACM TRANSACTIONS ON THE WEB, vol. 18 (issue 4)
See at:
dl.acm.org | CNR IRIS | CNR IRIS | CNR IRIS
2020
Journal article
Open Access
Crime and its fear in social media
Prieto Curiel R., Cresci S., Muntean C., Bishop S. R.
Social media posts incorporate real-time information that has, elsewhere, been exploited to predict social trends. This paper considers whether such information can be useful in relation to crime and fear of crime. A large number of tweets were collected from the 18 largest Spanish-speaking countries in Latin America, over a period of 70 days. These tweets are then classified as being crime-related or not and additional information is extracted, including the type of crime and where possible, any geo-location at a city level. From the analysis of collected data, it is established that around 15 out of every 1000 tweets have text related to a crime, or fear of crime. The frequency of tweets related to crime is then compared against the number of murders, the murder rate, or the level of fear of crime as recorded in surveys. Results show that, like mass media, such as newspapers, social media suffer from a strong bias towards violent or sexual crimes. Furthermore, social media messages are not highly correlated with crime. Thus, social media is shown not to be highly useful for detecting trends in crime itself, but what they do demonstrate is rather a reflection of the level of the fear of crime.Source: PALGRAVE COMMUNICATIONS, vol. 6 (issue 1)
Project(s): CIMPLEX, SoBigData
Prieto Curiel R., Cresci S., Muntean C., Bishop S. R.
Social media posts incorporate real-time information that has, elsewhere, been exploited to predict social trends. This paper considers whether such information can be useful in relation to crime and fear of crime. A large number of tweets were collected from the 18 largest Spanish-speaking countries in Latin America, over a period of 70 days. These tweets are then classified as being crime-related or not and additional information is extracted, including the type of crime and where possible, any geo-location at a city level. From the analysis of collected data, it is established that around 15 out of every 1000 tweets have text related to a crime, or fear of crime. The frequency of tweets related to crime is then compared against the number of murders, the murder rate, or the level of fear of crime as recorded in surveys. Results show that, like mass media, such as newspapers, social media suffer from a strong bias towards violent or sexual crimes. Furthermore, social media messages are not highly correlated with crime. Thus, social media is shown not to be highly useful for detecting trends in crime itself, but what they do demonstrate is rather a reflection of the level of the fear of crime.Source: PALGRAVE COMMUNICATIONS, vol. 6 (issue 1)
Project(s): CIMPLEX
, SoBigData
See at:
CNR IRIS | ISTI Repository | www.nature.com | CNR IRIS
2013
Conference article
Open Access
Learning to shorten query sessions.
Muntean C, Nardini F M, Silvestri F, Sydow M
We propose the use of learning to rank techniques to shorten query sessions by maximizing the probability that the query we predict is the final query of the current search session. We present a preliminary evaluation showing that this approach is a promising research direction.
Muntean C, Nardini F M, Silvestri F, Sydow M
We propose the use of learning to rank techniques to shorten query sessions by maximizing the probability that the query we predict is the final query of the current search session. We present a preliminary evaluation showing that this approach is a promising research direction.
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dl.acm.org | CNR IRIS | CNR IRIS
2012
Contribution to book
Restricted
Exploring the meaning behind Twitter hashtags through clustering.
Muntean C. I., Morar G. A., Moldovan D.
Social networks are generators of large amount of data produced by users, who are not limited with respect to the content of the information they exchange. The data generated can be a good indicator of trends and topic preferences among users. In our paper we focus on analyzing and representing hashtags by the corpus in which they appear. We cluster a large set of hashtags using K-means on map reduce in order to process data in a distributed manner. Our intention is to retrieve connections that might exist between different hashtags and their textual representation, and grasp their semantics through the main topics they occur with.Source: BIS 2012 - Business Information Systems Workshops. Revised papers, edited by Witold Abramowicz, John Domingue, Krzysztof W?cel, pp. 231–242. London: Springer, 2012
DOI: 10.1007/978-3-642-34228-8_22
Metrics:
Muntean C. I., Morar G. A., Moldovan D.
Social networks are generators of large amount of data produced by users, who are not limited with respect to the content of the information they exchange. The data generated can be a good indicator of trends and topic preferences among users. In our paper we focus on analyzing and representing hashtags by the corpus in which they appear. We cluster a large set of hashtags using K-means on map reduce in order to process data in a distributed manner. Our intention is to retrieve connections that might exist between different hashtags and their textual representation, and grasp their semantics through the main topics they occur with.Source: BIS 2012 - Business Information Systems Workshops. Revised papers, edited by Witold Abramowicz, John Domingue, Krzysztof W?cel, pp. 231–242. London: Springer, 2012
DOI: 10.1007/978-3-642-34228-8_22
Metrics:
See at:
doi.org | gateway.webofknowledge.com | link.springer.com | CNR ExploRA